Government AI
Public Sector AI Solutions
By connecting administrative data with your work systems, we build faster and more trustworthy public services.
YEJIN connects internal documents, statutes, regulations, and public data to AI, tailored to the administrative environments of central agencies, local governments, public institutions, and public enterprises.
By combining RAG Knowledge Retrieval, AI Orchestration, specialized AI Agents, data analysis and forecasting, and secure On-Premises technology, we build a tailored public-sector AI environment for citizen services, policy support, internal knowledge management, and administrative Workflow Automation.
- Administrative and public data integration
- Statute- and regulation-based RAG
- Citizen-service and consultation automation
- Policy and administrative work support
- Data analysis and forecasting
- On-Premises and air-gapped deployment
Challenges
Vast public data is not being connected to real administrative work
Public institutions hold large volumes of statutes, guidelines, administrative documents, and public data, but the materials are scattered across many systems. Staff spend a great deal of time on repetitive information search and document drafting, and citizen-service responses and policy judgments often still rely on individual experience and manual work.
Administrative knowledge is scattered
Statutes, guidelines, work manuals, and internal documents are spread across many systems, so finding and verifying the information you need takes a lot of time.
Repetitive administrative work is common
Drafting official documents, compiling materials, writing reports, preparing meeting materials, and statistical work repeat constantly, making it hard to focus on core policy work.
Citizen-service responses lack consistency
The content and quality of answers can vary by staff member and institution, and processing time increases as repetitive inquiries accumulate.
Data for policy judgment is fragmented
Departmental data and external public data are not connected, making it hard to grasp the current state, forecast demand, and analyze policy effects.
Accuracy and accountability of generative AI are a concern
Answers not grounded in statutes and official materials, or hallucinations, can undermine administrative trust and accountability.
Security and privacy protection are essential
Administrative information, internal materials, and personal data are difficult to send to external AI services, so a standard cloud AI cannot simply be adopted as is.
Our Approach
We connect public data, administrative work, and security systems into a single AI architecture
Public data integration
We connect and structure administrative documents, statutes, ordinances, business systems, public data, and internal databases so that AI can use them.
Statute- and regulation-based RAG
Grounded in official statutes, administrative guidelines, ordinances, internal rules, and work manuals, we retrieve information matching the meaning and context of a question and provide evidence-based answers.
Public Sector AI Agents
We design specialized AI Agents suited to each institution's procedures across citizen services, policy, administration, welfare, disaster response, contracts, and internal work.
AI Orchestration
According to the purpose of a question and its security level, we select the appropriate AI models, internal data, and work tools and control the process in an integrated way.
Data analysis and forecasting
We analyze population, welfare, transportation, citizen-service, facility, disaster, and policy data to forecast trends, risk factors, and policy demand.
Secure AI deployment
We build an independent AI environment based on On-Premises, Private Cloud, and sLM that can run on an internal network, a dedicated cloud, or an air-gapped network.
Solutions
Public Sector AI solution lineup
Administrative Knowledge Search AI
Based on statutes, ordinances, guidelines, work manuals, and internal documents, it quickly retrieves the administrative information you need and provides it with supporting evidence.
Citizen Service and Consultation AI
It automatically classifies citizen inquiries and retrieves relevant regulations and past cases to generate consistent draft answers.
Policy Support AI
It analyzes policy materials, statistics, research reports, and public data to organize the current state, key issues, and comparison materials needed for policymaking.
Administrative Document AI
It supports drafting, summarizing, comparing, and classifying official documents, reports, meeting materials, review opinions, and administrative documents.
Data Analysis and Forecasting AI
It integrates and analyzes administrative and public data to forecast changes in policy demand, citizen-service trends, facility usage, welfare demand, and risk factors.
Secure Private AI
It controls data and AI models directly on an institution's internal servers or air-gapped network, operating them securely in line with user permissions and security policies.
Use Cases
We design the scope of AI adoption to fit each institution’s administrative and policy work
Citizen-service automation
We classify citizen inquiries by type and retrieve relevant statutes, regulations, and cases to support staff with draft answers and processing direction.
Policy document analysis
We compare and analyze policy materials, research reports, statutes, and similar policies to structure key content, issues, and points for review.
Internal knowledge search
We connect departmental documents, work manuals, guidelines, and administrative cases into a single knowledge system so staff can quickly find the information they need.
Administrative work support
We support repetitive administrative document work such as drafting official documents, reports, meeting minutes, review materials, and press releases.
Welfare and health administration
We analyze welfare demand, beneficiary status, regional gaps, and service-usage data to support identifying policy targets and building program plans.
Disaster and safety management
We analyze weather, facility, report, incident, and regional data to detect risk indicators and support decisions on disaster response and facility management.
Urban, transportation, and facility management
We analyze traffic volume, facility usage, maintenance, population movement, and citizen-service data to improve the efficiency of urban operations and public-facility management.
Public enterprise operations support
We analyze contract, procurement, facility, customer, operations, and management data to support internal work and service-quality improvement at public enterprises.
Security Architecture
We answer the security the public sector demands with architecture
We keep data inside the institution and design AI to operate within the institution's security policies and work environment.
On-Premises deployment
We build the AI models, RAG search engine, and database on the institution's internal servers and air-gapped network to block external transmission.
Private Cloud configuration
In a dedicated cloud environment, we separate each institution's data and AI systems and operate them independently.
Proprietary sLM
We build a lightweight language model specialized in the institution's statutes, administrative terminology, and work documents, reducing dependence on external APIs.
Access control
We granularly control access to documents and AI features by department, rank, task, and security level.
Audit log management
We record queries, searches, document access, generated results, and user activity history to support auditing and accountability.
Privacy protection
We identify and mask personal and sensitive information and apply the scope of data processing and retention policies in line with the institution's privacy standards.
Trustworthy AI
We design not only accuracy but also evidence, accountability, and traceability
Evidence-based responses
We present the sources of statutes, guidelines, documents, and data used in AI answers.
Multi-model cross review
For important administrative information, we compare and analyze the results of multiple AI models to check for errors, omissions, and logical contradictions.
Approval-based processing
We design documents and answers generated by AI to be reflected in real work only after staff review and approval.
Version and history management
We manage the revision history of statutes, regulations, and knowledge data to provide answers aligned with the latest standards.
Responsible AI operation
We clearly define the role and limits of AI and ensure that sensitive decisions go through the final judgment of the responsible official or institution.
Audit and trace logs
We record AI queries, searches, referenced documents, generated results, and user review and approval history so that the process can be verified afterward.
Deployment
Deployment models
Cloud SaaS
Applied to general administrative-support work with a lower security level that needs fast adoption.
Private Cloud
Operate data and AI services independently in a cloud dedicated to the institution.
On-Premises
Control AI models and data directly on internal, air-gapped networks and your own servers.
Hybrid AI
A hybrid architecture that processes security-critical data with an internal sLM and handles general work with an external LLM.
Process
Adoption process
- 1
Work and security assessment
Analyze the institution's administrative work, data, systems, personal information, and security policy
- 2
Priority selection
Select priority tasks such as citizen services, knowledge search, and document administration that can be validated quickly
- 3
PoC validation
Validate accuracy, security, and work impact using real administrative documents and data
- 4
System build
Build data integration, RAG, AI Agents, admin features, access control, and audit logs
- 5
Operation and enhancement
Analyze usage, accuracy, processing time, and user satisfaction to continuously improve
Impact
The administrative change you can expect from adopting public sector AI
The figures below are industry benchmark-type indicators drawn from domestic and international research and adoption cases related to generative AI and administrative Workflow Automation.
Potential reduction in time for citizen-inquiry classification, information search, and draft-answer writing
Potential reduction in time to search and review statutes, regulations, and internal documents
Potential time savings on repetitive document work such as official documents, reports, and meeting materials
Potential productivity gains in collecting, comparing, and summarizing policy materials
Ability to build an always-on response system for repetitive inquiries and information guidance
Ability to trace the history of AI queries, searches, document access, and generated results
* The figures above are industry benchmark-type indicators drawn from domestic and international research and adoption cases related to generative AI and administrative Workflow Automation. Actual results may vary depending on the institution's data quality, target tasks, the scope of system integration, security environment, and mode of operation.
View references
- · Domestic and international research on generative AI-based administrative Workflow Automation
- · Public-sector AI adoption and citizen-service efficiency cases
- · Industry benchmarks for automating document, search, and consultation work
Frequently Asked Questions
It can be applied to citizen-service and consultation support, internal administrative-document search, statute and guideline analysis, policy-material summarization, report drafting, public-data analysis, repetitive administrative Workflow Automation, and internal knowledge search.
While general generative AI answers based on public general knowledge, the public sector AI solution connects an institution's internal documents, statutes, work guidelines, policy materials, and public data to provide evidence-based answers suited to the institution's work. User permissions, audit logs, and approval procedures can be applied together.
Yes. Work handbooks, regulations, guidelines, official documents, reports, meeting materials, and policy materials can be connected via RAG and vector search. Searchable documents and answer scope can be differentiated by user permission.
Yes. Statutes, ordinances, public notices, guidelines, and internal rules can be connected so that relevant clauses and supporting materials are retrieved quickly. That said, work requiring legal judgment should be designed to go through the final review and approval of the responsible official.
It can be used for classifying citizen inquiries, recommending the responsible department, searching similar cases, drafting answers, and providing processing-status updates. Rather than having AI make final judgments, it is configured to support the responsible official's review and case handling.
Minimal collection of personal information, access control, encryption in transit and at rest, audit logs, and restrictions on data export can be applied. As needed, connections to external AI services can be limited, and the system can be built as a Private Cloud, On-Premises, or air-gapped environment.
Yes. For institutions with restricted external internet access, we can build an On-Premises and air-gapped AI environment using internal servers, dedicated GPUs, a local database, and an sLM. We design the integration approach to match the institution's network separation and security policy.
Yes. We can integrate after reviewing the APIs and data structures of electronic approval, records management, websites, citizen-service systems, databases, groupware, and internal work portals. The actual integration scope may vary depending on system access permissions and security policy.
Yes. We can display the documents and sources used in an answer and record queries, referenced materials, generated results, and user and administrator change history as audit logs. This increases the transparency of work processing and after-the-fact traceability.
It generally proceeds in the order of work assessment, data and security-environment analysis, PoC, system design, development and integration, user validation, and transition to operation. A single-task PoC takes about 4-8 weeks and an integrated build about 3-6 months or more; the exact timeline is set after data and security review.
We design safe, trustworthy public-sector AI suited to your institution's environment
YEJIN does not simply adopt AI; we connect the institution's administrative work, public data, statutory framework, and security policy into a single AI architecture. From preliminary work assessment to PoC, On-Premises build, and operational enhancement, we systematically support the public sector's AI transformation.